A testing-based approach to the discovery of differentially correlated variable sets

TL;DR

This paper introduces Differential Correlation Mining (DCM), a novel method for identifying variable sets with different correlation structures across two conditions, useful in genomics and brain imaging.

Contribution

The paper presents a new iterative hypothesis testing approach for detecting differentially correlated variable sets, advancing differential analysis techniques.

Findings

DCM effectively identifies differentially correlated sets in simulated data.

Application to genomics data reveals biologically relevant variable sets.

Application to brain imaging data uncovers condition-specific correlation patterns.

Abstract

Given data obtained under two sampling conditions, it is often of interest to identify variables that behave differently in one condition than in the other. We introduce a method for differential analysis of second-order behavior called Differential Correlation Mining (DCM). The DCM method identifies differentially correlated sets of variables, with the property that the average pairwise correlation between variables in a set is higher under one sample condition than the other. DCM is based on an iterative search procedure that adaptively updates the size and elements of a candidate variable set. Updates are performed via hypothesis testing of individual variables, based on the asymptotic distribution of their average differential correlation. We investigate the performance of DCM by applying it to simulated data as well as recent experimental datasets in genomics and brain imaging.